Sensing and Computer Vision-Aided Mobility Management for 6G Millimeter and Terahertz Communication Systems

Millimeter wave (mmWave) and terahertz (THz) communications have been considered as the key techniques to support extremely high data rates in the 6G system. One main limitation of the mmWave/THz communications is the severe path loss and low penetration power. For these reasons, it is expected that mmWave/THz communication will be mainly employed in the ultra-dense network (UDN) environment. In order to get the most out of the mmWave/THz UDN, a mobile should be associated to the base stations (BSs) providing a high quality-of-service (QoS). This task is challenging since the reliable path can be disappeared even with a small movement of a mobile. An aim of this paper is to propose a novel mobility management technique based on sensing and computer vision (CV). Our key idea is to predict the cell association from the visual sensing information and CV-based inference and decision. By extracting the geometric information of a mobile from the image and then using it for the downlink rate prediction, we preemptively switch the cell association in UDN. From the numerical evaluations on the realistic mmWave/THz UDN environments, we show that the proposed scheme achieves more than 30% throughput gain over the conventional mobility management techniques.